A Measure of Semantic Similarity between GO Terms Based on Semantic Contributions of Their Ancestors

It is generally believed that semantic similarity between GO terms is critical in resolving semantic heterogeneousness when integrating heterogeneous biological databases. In this paper, we propose an approach to determine the semantic similarity of two GO terms based on both the locations of these terms in the GO graph and their semantic relations with their ancestor terms. First, we collect all their ancestor terms (including the specific terms) as sets T_A and T_B respectively. Second, we calculate the intersection of T_A and T_B, aggregate the largest semantic contribution of each term in the intersection, meanwhile compute the union of T_A and T_B, aggregate the minimum contribution of each term in the union. The ratio of these two sums is used to be the semantic similarity between the two GO terms, and the outcomes of our method are consistent with human perspectives.